{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "51fc35a7",
   "metadata": {},
   "source": [
    "## 1. Importing libraries and loading dataset\n",
    "- the dataset is in SQLite3 form"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "50e0afa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'1.20.3'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing the neccessary libraries\n",
    "\n",
    "# Tensorflow and Keras library\n",
    "###############################################################\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Flatten, LSTM, Dense, Dropout\n",
    "from keras.regularizers import l2 #regularization function\n",
    "from tensorflow.keras.optimizers import Adam  #perform gradient descent\n",
    "from tensorflow.math import reduce_prod\n",
    "print(tf.__version__)\n",
    "###############################################################\n",
    "# Supporting libraries\n",
    "###############################################################\n",
    "import pandas as pd #used to read dataset and preform dataframe operations\n",
    "import numpy as np  # perform basic array operations\n",
    "import sqlite3      # our raw data is in SQLite form\n",
    "import matplotlib.pyplot as plt #matplotlib\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder #s scaler is for normalizing data and label encoder is to label.\n",
    "\n",
    "np.version.version\n",
    "\n",
    "###############################################################\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "207b841e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.20.3'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ff3640e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.3.1'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a8653c",
   "metadata": {},
   "source": [
    "### 1.1 Import the dataset (SQLite file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c4b683ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unit</th>\n",
       "      <th>TIME</th>\n",
       "      <th>ACCEL_X</th>\n",
       "      <th>ACCEL_Y</th>\n",
       "      <th>ACCEL_Z</th>\n",
       "      <th>GYRO_X</th>\n",
       "      <th>GYRO_Y</th>\n",
       "      <th>GYRO_Z</th>\n",
       "      <th>CURRENT_SPEED</th>\n",
       "      <th>ACTIVITY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>09:54:08</td>\n",
       "      <td>0.028730</td>\n",
       "      <td>9.651326</td>\n",
       "      <td>-0.098162</td>\n",
       "      <td>-0.001145</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>09:54:08</td>\n",
       "      <td>0.028730</td>\n",
       "      <td>9.651326</td>\n",
       "      <td>-0.098162</td>\n",
       "      <td>-0.001145</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>09:54:08</td>\n",
       "      <td>0.122104</td>\n",
       "      <td>9.646538</td>\n",
       "      <td>-0.663194</td>\n",
       "      <td>-0.001145</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>09:54:08</td>\n",
       "      <td>0.122104</td>\n",
       "      <td>9.646538</td>\n",
       "      <td>-0.663194</td>\n",
       "      <td>-0.001145</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>09:54:08</td>\n",
       "      <td>0.112527</td>\n",
       "      <td>9.639356</td>\n",
       "      <td>-0.962469</td>\n",
       "      <td>-0.001145</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unit      TIME   ACCEL_X   ACCEL_Y   ACCEL_Z    GYRO_X    GYRO_Y    GYRO_Z  \\\n",
       "0     1  09:54:08  0.028730  9.651326 -0.098162 -0.001145  0.001833  0.000611   \n",
       "1     2  09:54:08  0.028730  9.651326 -0.098162 -0.001145  0.001833  0.000611   \n",
       "2     3  09:54:08  0.122104  9.646538 -0.663194 -0.001145  0.000000 -0.000611   \n",
       "3     4  09:54:08  0.122104  9.646538 -0.663194 -0.001145  0.000000 -0.000611   \n",
       "4     5  09:54:08  0.112527  9.639356 -0.962469 -0.001145  0.000000 -0.000611   \n",
       "\n",
       "   CURRENT_SPEED ACTIVITY  \n",
       "0            0.0    Right  \n",
       "1            0.0    Right  \n",
       "2            0.0    Right  \n",
       "3            0.0    Right  \n",
       "4            0.0    Right  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = open('TESTDATA_real.db')   ##open the file \n",
    "conn = sqlite3.connect('TESTDATA_real.db')  ## create the cursor pointing to the table\n",
    "df = pd.read_sql_query('SELECT * FROM SENSOR_TABLE', conn) #create pandas dataframe so we can manipulate\n",
    "df.insert(0, 'unit', range(1, 1+len(df))) # add unit column\n",
    "df.head() # first 5 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "69821eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6448, 10)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape ## 10 data column 6448 rows down"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f4611bf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6448 entries, 0 to 6447\n",
      "Data columns (total 10 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   unit           6448 non-null   int64  \n",
      " 1   TIME           6448 non-null   object \n",
      " 2   ACCEL_X        6448 non-null   float64\n",
      " 3   ACCEL_Y        6448 non-null   float64\n",
      " 4   ACCEL_Z        6448 non-null   float64\n",
      " 5   GYRO_X         6448 non-null   float64\n",
      " 6   GYRO_Y         6448 non-null   float64\n",
      " 7   GYRO_Z         6448 non-null   float64\n",
      " 8   CURRENT_SPEED  6448 non-null   float64\n",
      " 9   ACTIVITY       6448 non-null   object \n",
      "dtypes: float64(7), int64(1), object(2)\n",
      "memory usage: 503.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a08ead79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unit</th>\n",
       "      <th>TIME</th>\n",
       "      <th>ACCEL_X</th>\n",
       "      <th>ACCEL_Y</th>\n",
       "      <th>ACCEL_Z</th>\n",
       "      <th>GYRO_X</th>\n",
       "      <th>GYRO_Y</th>\n",
       "      <th>GYRO_Z</th>\n",
       "      <th>CURRENT_SPEED</th>\n",
       "      <th>ACTIVITY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6443</th>\n",
       "      <td>6444</td>\n",
       "      <td>10:18:39</td>\n",
       "      <td>0.251391</td>\n",
       "      <td>9.988909</td>\n",
       "      <td>0.172383</td>\n",
       "      <td>0.010461</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6444</th>\n",
       "      <td>6445</td>\n",
       "      <td>10:18:39</td>\n",
       "      <td>0.263362</td>\n",
       "      <td>9.890746</td>\n",
       "      <td>-0.387861</td>\n",
       "      <td>-0.009697</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6445</th>\n",
       "      <td>6446</td>\n",
       "      <td>10:18:39</td>\n",
       "      <td>0.263362</td>\n",
       "      <td>9.890746</td>\n",
       "      <td>-0.387861</td>\n",
       "      <td>-0.009697</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6446</th>\n",
       "      <td>6447</td>\n",
       "      <td>10:18:39</td>\n",
       "      <td>0.014365</td>\n",
       "      <td>9.522039</td>\n",
       "      <td>-1.103727</td>\n",
       "      <td>0.004963</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6447</th>\n",
       "      <td>6448</td>\n",
       "      <td>10:18:39</td>\n",
       "      <td>0.014365</td>\n",
       "      <td>9.522039</td>\n",
       "      <td>-1.103727</td>\n",
       "      <td>0.004963</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brake</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      unit      TIME   ACCEL_X   ACCEL_Y   ACCEL_Z    GYRO_X    GYRO_Y  \\\n",
       "6443  6444  10:18:39  0.251391  9.988909  0.172383  0.010461  0.000611   \n",
       "6444  6445  10:18:39  0.263362  9.890746 -0.387861 -0.009697  0.000000   \n",
       "6445  6446  10:18:39  0.263362  9.890746 -0.387861 -0.009697  0.000000   \n",
       "6446  6447  10:18:39  0.014365  9.522039 -1.103727  0.004963  0.000611   \n",
       "6447  6448  10:18:39  0.014365  9.522039 -1.103727  0.004963  0.000611   \n",
       "\n",
       "        GYRO_Z  CURRENT_SPEED ACTIVITY  \n",
       "6443  0.000000            0.0    Brake  \n",
       "6444 -0.000611            0.0    Brake  \n",
       "6445 -0.000611            0.0    Brake  \n",
       "6446  0.000000            0.0    Brake  \n",
       "6447  0.000000            0.0    Brake  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail() # list last 5 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3af4a612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unit             0\n",
       "TIME             0\n",
       "ACCEL_X          0\n",
       "ACCEL_Y          0\n",
       "ACCEL_Z          0\n",
       "GYRO_X           0\n",
       "GYRO_Y           0\n",
       "GYRO_Z           0\n",
       "CURRENT_SPEED    0\n",
       "ACTIVITY         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()  #check for NULL values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "89811379",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Left          1692\n",
       "Accelerate    1668\n",
       "Right         1658\n",
       "Brake         1430\n",
       "Name: ACTIVITY, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ACTIVITY'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9fa7667",
   "metadata": {},
   "source": [
    "## 2 Visualizing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0de4fb24",
   "metadata": {},
   "source": [
    "### 2.1 Accelerometer plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f508be61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d540ec91f0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Accelerometer plot \n",
    "%matplotlib inline\n",
    "# RIGHT TURN \n",
    "plt.plot(np.arange(0,150), df.ACCEL_X[df['ACTIVITY']==\"Right\"][:150],  label = 'X-axis' )\n",
    "plt.plot(np.arange(0,150), df.ACCEL_Y[df['ACTIVITY']==\"Right\"][:150],  label = 'Y-axis' )\n",
    "plt.plot(np.arange(0,150), df.ACCEL_Z[df['ACTIVITY']==\"Right\"][:150],  label = 'Z-axis' )\n",
    "plt.title(\"Right Turn - Accelerometer\")\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a942fa8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d540fd7220>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LEFT TURN\n",
    "plt.plot(np.arange(0,150), df.ACCEL_X[df['ACTIVITY']==\"Left\"][:150],  label = 'X-axis' )\n",
    "plt.plot(np.arange(0,150), df.ACCEL_Y[df['ACTIVITY']==\"Left\"][:150],  label = 'Y-axis' )\n",
    "plt.plot(np.arange(0,150), df.ACCEL_Z[df['ACTIVITY']==\"Left\"][:150],  label = 'Z-axis' )\n",
    "plt.title(\"Left Turn - Accelerometer\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3c8b737e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d54107ea90>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ACCELERATION\n",
    "plt.plot(np.arange(0,150), df.ACCEL_X[df['ACTIVITY']==\"Accelerate\"][:150],  label = 'X-axis' )\n",
    "plt.plot(np.arange(0,150), df.ACCEL_Y[df['ACTIVITY']==\"Accelerate\"][:150],  label = 'Y-axis' )\n",
    "plt.plot(np.arange(0,150), df.ACCEL_Z[df['ACTIVITY']==\"Accelerate\"][:150],  label = 'Z-axis' )\n",
    "plt.title(\"Accelerate - Accelerometer\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3cf6d630",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d540e57760>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BRAKING\n",
    "plt.plot(np.arange(0,150), df.ACCEL_X[df['ACTIVITY']==\"Brake\"][:150],  label = 'X-axis' )\n",
    "plt.plot(np.arange(0,150), df.ACCEL_Y[df['ACTIVITY']==\"Brake\"][:150],  label = 'Y-axis' )\n",
    "plt.plot(np.arange(0,150), df.ACCEL_Z[df['ACTIVITY']==\"Brake\"][:150],  label = 'Z-axis' )\n",
    "plt.title(\"Brake - Accelerometer\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "562ee9e1",
   "metadata": {},
   "source": [
    "### 2.2 Gyroscope plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2221abc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d54125c190>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gyroscope plot\n",
    "\n",
    "# RIGHT TURN \n",
    "plt.plot(np.arange(0,150), df.GYRO_X[df['ACTIVITY']==\"Right\"][:150],  label = 'X-axis' )\n",
    "plt.plot(np.arange(0,150), df.GYRO_Y[df['ACTIVITY']==\"Right\"][:150],  label = 'Y-axis' )\n",
    "plt.plot(np.arange(0,150), df.GYRO_Z[df['ACTIVITY']==\"Right\"][:150],  label = 'Z-axis' )\n",
    "plt.title(\"Right Turn - Gyroscope\")\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e2b03e06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d5412ef220>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LEFT TURN\n",
    "plt.plot(np.arange(0,150), df.GYRO_X[df['ACTIVITY']==\"Left\"][:150],  label = 'X-axis' )\n",
    "plt.plot(np.arange(0,150), df.GYRO_Y[df['ACTIVITY']==\"Left\"][:150],  label = 'Y-axis' )\n",
    "plt.plot(np.arange(0,150), df.GYRO_Z[df['ACTIVITY']==\"Left\"][:150],  label = 'Z-axis' )\n",
    "plt.title(\"Left turn - Gyroscope\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "47cd3c2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d54137b340>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ACCELERATE\n",
    "plt.plot(np.arange(0,150), df.GYRO_X[df['ACTIVITY']==\"Accelerate\"][:150],  label = 'X-axis' )\n",
    "plt.plot(np.arange(0,150), df.GYRO_Y[df['ACTIVITY']==\"Accelerate\"][:150],  label = 'Y-axis' )\n",
    "plt.plot(np.arange(0,150), df.GYRO_Z[df['ACTIVITY']==\"Accelerate\"][:150],  label = 'Z-axis' )\n",
    "plt.title(\"Accelerate - Gyroscope\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4a2ab544",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d5413f9250>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BRAKE\n",
    "plt.plot(np.arange(0,150), df.GYRO_X[df['ACTIVITY']==\"Brake\"][:150],  label = 'X-axis' )\n",
    "plt.plot(np.arange(0,150), df.GYRO_Y[df['ACTIVITY']==\"Brake\"][:150],  label = 'Y-axis' )\n",
    "plt.plot(np.arange(0,150), df.GYRO_Z[df['ACTIVITY']==\"Brake\"][:150],  label = 'Z-axis' )\n",
    "plt.title(\"Brake - Gyroscope\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c70d95fd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Left', 'Accelerate', 'Right', 'Brake'], dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ACTIVITY'].value_counts().index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8fdd345",
   "metadata": {},
   "source": [
    "## 3 Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61fe17c8",
   "metadata": {},
   "source": [
    "### Splitting data and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "28389d9b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ACCEL_X</th>\n",
       "      <th>ACCEL_Y</th>\n",
       "      <th>ACCEL_Z</th>\n",
       "      <th>GYRO_X</th>\n",
       "      <th>GYRO_Y</th>\n",
       "      <th>GYRO_Z</th>\n",
       "      <th>CURRENT_SPEED</th>\n",
       "      <th>ACTIVITY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.028730</td>\n",
       "      <td>9.651326</td>\n",
       "      <td>-0.098162</td>\n",
       "      <td>-0.001145</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.028730</td>\n",
       "      <td>9.651326</td>\n",
       "      <td>-0.098162</td>\n",
       "      <td>-0.001145</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.122104</td>\n",
       "      <td>9.646538</td>\n",
       "      <td>-0.663194</td>\n",
       "      <td>-0.001145</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.122104</td>\n",
       "      <td>9.646538</td>\n",
       "      <td>-0.663194</td>\n",
       "      <td>-0.001145</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.112527</td>\n",
       "      <td>9.639356</td>\n",
       "      <td>-0.962469</td>\n",
       "      <td>-0.001145</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6443</th>\n",
       "      <td>0.251391</td>\n",
       "      <td>9.988909</td>\n",
       "      <td>0.172383</td>\n",
       "      <td>0.010461</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6444</th>\n",
       "      <td>0.263362</td>\n",
       "      <td>9.890746</td>\n",
       "      <td>-0.387861</td>\n",
       "      <td>-0.009697</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6445</th>\n",
       "      <td>0.263362</td>\n",
       "      <td>9.890746</td>\n",
       "      <td>-0.387861</td>\n",
       "      <td>-0.009697</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6446</th>\n",
       "      <td>0.014365</td>\n",
       "      <td>9.522039</td>\n",
       "      <td>-1.103727</td>\n",
       "      <td>0.004963</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6447</th>\n",
       "      <td>0.014365</td>\n",
       "      <td>9.522039</td>\n",
       "      <td>-1.103727</td>\n",
       "      <td>0.004963</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brake</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6448 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ACCEL_X   ACCEL_Y   ACCEL_Z    GYRO_X    GYRO_Y    GYRO_Z  \\\n",
       "0     0.028730  9.651326 -0.098162 -0.001145  0.001833  0.000611   \n",
       "1     0.028730  9.651326 -0.098162 -0.001145  0.001833  0.000611   \n",
       "2     0.122104  9.646538 -0.663194 -0.001145  0.000000 -0.000611   \n",
       "3     0.122104  9.646538 -0.663194 -0.001145  0.000000 -0.000611   \n",
       "4     0.112527  9.639356 -0.962469 -0.001145  0.000000 -0.000611   \n",
       "...        ...       ...       ...       ...       ...       ...   \n",
       "6443  0.251391  9.988909  0.172383  0.010461  0.000611  0.000000   \n",
       "6444  0.263362  9.890746 -0.387861 -0.009697  0.000000 -0.000611   \n",
       "6445  0.263362  9.890746 -0.387861 -0.009697  0.000000 -0.000611   \n",
       "6446  0.014365  9.522039 -1.103727  0.004963  0.000611  0.000000   \n",
       "6447  0.014365  9.522039 -1.103727  0.004963  0.000611  0.000000   \n",
       "\n",
       "      CURRENT_SPEED ACTIVITY  \n",
       "0               0.0    Right  \n",
       "1               0.0    Right  \n",
       "2               0.0    Right  \n",
       "3               0.0    Right  \n",
       "4               0.0    Right  \n",
       "...             ...      ...  \n",
       "6443            0.0    Brake  \n",
       "6444            0.0    Brake  \n",
       "6445            0.0    Brake  \n",
       "6446            0.0    Brake  \n",
       "6447            0.0    Brake  \n",
       "\n",
       "[6448 rows x 8 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = df[df.columns[2:11]]\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "08cba481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6448, 8)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b6700567",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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     "execution_count": 21,
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   "source": [
    "y_train = df[df.columns[10:11]]\n",
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9761dcb9",
   "metadata": {
    "scrolled": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6448, 0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "y_train.shape"
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     "execution_count": 23,
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   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fbfc062b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6448, 8)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fddbc9c7",
   "metadata": {},
   "source": [
    "### Create time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9aaf3f6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "\n",
    "def create_dataset(X, y, time_steps=1, step=1):\n",
    "    Xs, ys = [], []\n",
    "    for i in range(0, len(X) - time_steps, step):\n",
    "        v = X.iloc[i:(i + time_steps)].values\n",
    "        labels = y.iloc[i: i + time_steps]\n",
    "        Xs.append(v)        \n",
    "        ys.append(stats.mode(labels)[0][0])\n",
    "    return np.array(Xs), np.array(ys).reshape(-1, 1)\n",
    "\n",
    "TIME_STEPS = 50\n",
    "STEP = 6\n",
    "\n",
    "X_train, y_train = create_dataset(\n",
    "    X_train[['ACCEL_X', 'ACCEL_Y', 'ACCEL_Z', 'GYRO_X', 'GYRO_Y', 'GYRO_Z']], \n",
    "    X_train.ACTIVITY, \n",
    "    TIME_STEPS, \n",
    "    STEP\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7a5fb2e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1067, 50, 6), (1067, 1))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9e5d94bc",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Right'],\n",
       "       ['Right'],\n",
       "       ['Right'],\n",
       "       ...,\n",
       "       ['Brake'],\n",
       "       ['Brake'],\n",
       "       ['Brake']], dtype='<U10')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ec01b0ee",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['Right']\n",
      " ['Right']\n",
      " ['Right']\n",
      " ...\n",
      " ['Brake']\n",
      " ['Brake']\n",
      " ['Brake']]\n"
     ]
    }
   ],
   "source": [
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2dc6bd7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1067, 50, 6)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "450e885f",
   "metadata": {},
   "source": [
    "## One hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "19981864",
   "metadata": {},
   "outputs": [],
   "source": [
    "enc = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
    "enc = enc.fit(y_train)\n",
    "y_train = enc.transform(y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "789e1a08",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 1.],\n",
       "       [0., 0., 0., 1.],\n",
       "       [0., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 1., 0., 0.],\n",
       "       [0., 1., 0., 0.],\n",
       "       [0., 1., 0., 0.]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "23b14441",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1067, 50, 6) (1067, 4)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "389d2e14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Accelerate', 'Brake', 'Left', 'Right'], dtype='<U10')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc.categories_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "62b9b480",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc.sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "07318ea6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. 1.]\n",
      " [0. 0. 0. 1.]\n",
      " [0. 0. 0. 1.]\n",
      " ...\n",
      " [0. 1. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 1. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8cb23cd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Accelerate' 'Brake' 'Left' 'Right']\n"
     ]
    }
   ],
   "source": [
    "print(enc.categories_[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3460e635",
   "metadata": {},
   "source": [
    "#### 1.4.4 Split into training data and validation data  !!!! try use train test split keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d7618f3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.3, random_state=0, stratify=y_train )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8555476d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(746, 50, 6) (746, 4)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "79e69887",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c9e15151",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e66bc5ee",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.ndim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af0db70f",
   "metadata": {},
   "source": [
    "#### The labels are encoded as follows:\n",
    "- [0,0,0,1] = Right\n",
    "- [0,0,1,0] = Left\n",
    "- [0,1,0,0] = Brake\n",
    "- [1,0,0,0] = Accelerate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d290c14",
   "metadata": {},
   "source": [
    "## Scaling\n",
    "-StandardScale() will make the:\n",
    "- mean = 0\n",
    "- variance = 1\n",
    "\n",
    "https://towardsdatascience.com/what-and-why-behind-fit-transform-vs-transform-in-scikit-learn-78f915cf96fe\n",
    "\n",
    "how to make 3d input inside standard scaler:\n",
    "https://newbedev.com/how-to-standard-scale-a-3d-matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "952882e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "# standardscaler only accept 2D, so we need to convert it into 3D then back to 3D\n",
    "# we only use fit_transform() on training data, so that it will remember the mean and std deviation which will be used in the testing set\n",
    "X_train_scaled = scaler.fit_transform(X_train.reshape(-1, X_train.shape[-1])).reshape(X_train.shape)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b8525393",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(746, 50, 6)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled.shape  #back to 3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ae9ff572",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-9.66795543e-04,  9.80119899e+00, -2.42860247e-01, -4.09775121e-03,\n",
       "        2.30926722e-03, -1.42478996e-03])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.mean_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "046b367f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(746, 50, 6)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7faf982a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.20035490e-02  9.78139953e+00 -2.02239437e-01 -4.23572813e-03\n",
      "  -2.88400432e-03 -1.72942012e-03]\n",
      " [-2.20035490e-02  9.78139953e+00 -2.02239437e-01 -4.23900354e-03\n",
      "  -2.88727973e-03 -1.68602084e-03]\n",
      " [ 1.54050490e-03  9.80292487e+00 -2.31753749e-01 -4.31433815e-03\n",
      "  -1.70567337e-03 -9.86719314e-04]\n",
      " [ 1.54050490e-03  9.80292487e+00 -2.31753749e-01 -4.48384097e-03\n",
      "  -1.78018911e-03 -1.13247536e-03]\n",
      " [-1.08637704e-02  9.83823453e+00 -2.11979923e-01 -3.74523449e-03\n",
      "  -8.92550981e-04 -1.79247186e-03]\n",
      " [-1.08637704e-02  9.83823453e+00 -2.11979923e-01 -3.82138793e-03\n",
      "  -7.89375322e-04 -1.69011508e-03]\n",
      " [-4.24922681e-02  9.78131607e+00 -2.37254236e-01 -4.32907750e-03\n",
      "   1.41825556e-03 -2.19043503e-03]\n",
      " [-4.24922681e-02  9.78131607e+00 -2.37254236e-01 -4.32170781e-03\n",
      "   1.43135724e-03 -2.20189900e-03]\n",
      " [-1.61400002e-02  9.80883334e+00 -2.42587834e-01 -4.32416441e-03\n",
      "   2.71613945e-03 -1.06860473e-03]\n",
      " [-1.61400002e-02  9.80883334e+00 -2.42587834e-01 -4.52232713e-03\n",
      "   2.76281414e-03 -1.20044025e-03]\n",
      " [-1.20223577e-02  9.83769856e+00 -2.07836606e-01 -3.52004957e-03\n",
      "   4.20645448e-03 -1.30689133e-03]\n",
      " [-1.20223577e-02  9.83769856e+00 -2.07836606e-01 -3.58146364e-03\n",
      "   4.31781868e-03 -1.19552715e-03]\n",
      " [-5.39401460e-02  9.76598485e+00 -2.67034126e-01 -3.33908275e-03\n",
      "   6.30026455e-03 -2.53517267e-03]\n",
      " [-5.39401460e-02  9.76598485e+00 -2.67034126e-01 -3.33171306e-03\n",
      "   6.31336622e-03 -2.54663663e-03]\n",
      " [-3.60221445e-02  9.80947522e+00 -2.48791977e-01 -2.95913445e-03\n",
      "   7.50234238e-03 -1.14557705e-03]\n",
      " [-3.60221445e-02  9.80947522e+00 -2.48791977e-01 -3.12863727e-03\n",
      "   7.42782664e-03 -1.29133310e-03]\n",
      " [-1.51515088e-02  9.82733867e+00 -2.57980047e-01 -3.59128990e-03\n",
      "   8.82561065e-03 -1.64999122e-03]\n",
      " [-1.51515088e-02  9.82733867e+00 -2.57980047e-01 -3.66744334e-03\n",
      "   8.92878631e-03 -1.54763444e-03]\n",
      " [-4.36669016e-02  9.77669135e+00 -2.62524538e-01 -4.43798513e-03\n",
      "   9.98347069e-03 -2.58757936e-03]\n",
      " [-4.36669016e-02  9.77669135e+00 -2.62524538e-01 -4.43389086e-03\n",
      "   9.99329695e-03 -2.55564405e-03]\n",
      " [-1.67786685e-02  9.79898054e+00 -2.68847830e-01 -4.43307202e-03\n",
      "   1.01668939e-02 -1.05550308e-03]\n",
      " [-1.67786685e-02  9.79898054e+00 -2.68847830e-01 -4.60257484e-03\n",
      "   1.00923782e-02 -1.20125913e-03]\n",
      " [ 2.61789600e-02  9.81668672e+00 -2.40639737e-01 -4.83676716e-03\n",
      "   8.54228751e-03  2.63671096e-04]\n",
      " [ 2.61789600e-02  9.81668672e+00 -2.40639737e-01 -4.88098528e-03\n",
      "   8.64955744e-03  3.14440060e-04]\n",
      " [-3.22928391e-02  9.77240360e+00 -2.71819320e-01 -3.96714396e-03\n",
      "   8.10338125e-03 -2.39023545e-03]\n",
      " [-3.22928391e-02  9.77240360e+00 -2.71819320e-01 -3.96304969e-03\n",
      "   8.11320751e-03 -2.35830014e-03]\n",
      " [ 1.78826965e-02  9.79878797e+00 -2.54212229e-01 -4.02282607e-03\n",
      "   6.59750826e-03 -8.86819080e-04]\n",
      " [ 1.78826965e-02  9.79878797e+00 -2.54212229e-01 -4.03265232e-03\n",
      "   6.47631783e-03 -9.15478976e-04]\n",
      " [ 4.29736751e-02  9.82784896e+00 -2.56157117e-01 -3.99826044e-03\n",
      "   4.84925486e-03 -1.16277299e-03]\n",
      " [ 4.29736751e-02  9.82784896e+00 -2.56157117e-01 -4.04247857e-03\n",
      "   4.95652479e-03 -1.11200402e-03]\n",
      " [ 8.82580930e-03  9.78512242e+00 -2.54488637e-01 -4.03101456e-03\n",
      "   3.24102493e-03 -1.65981750e-03]\n",
      " [ 8.82580930e-03  9.78512242e+00 -2.54488637e-01 -4.02692029e-03\n",
      "   3.25085119e-03 -1.62788219e-03]\n",
      " [ 4.32561001e-02  9.80406742e+00 -2.39558976e-01 -3.72148770e-03\n",
      "   1.40761063e-03 -4.14340239e-04]\n",
      " [ 4.32561001e-02  9.80406742e+00 -2.39558976e-01 -3.87051916e-03\n",
      "   1.31180469e-03 -5.83843059e-04]\n",
      " [ 4.92833267e-02  9.82338471e+00 -2.29682894e-01 -3.94257835e-03\n",
      "  -6.82105466e-04 -1.40924809e-03]\n",
      " [ 4.92833267e-02  9.82338471e+00 -2.29682894e-01 -3.98679648e-03\n",
      "  -5.74835537e-04 -1.35847912e-03]\n",
      " [-2.29182241e-02  9.78262552e+00 -2.42424556e-01 -4.61158221e-03\n",
      "  -1.27823144e-03 -3.03631146e-03]\n",
      " [-2.29182241e-02  9.78262552e+00 -2.42424556e-01 -4.60748794e-03\n",
      "  -1.26840518e-03 -3.00437615e-03]\n",
      " [ 2.14547432e-02  9.80056596e+00 -2.35507929e-01 -4.22835846e-03\n",
      "  -2.35420578e-03 -6.52626834e-04]\n",
      " [ 2.14547432e-02  9.80056596e+00 -2.35507929e-01 -4.39786128e-03\n",
      "  -2.42872152e-03 -7.98382884e-04]\n",
      " [ 3.23473980e-02  9.83692190e+00 -2.30584732e-01 -3.95076690e-03\n",
      "  -2.94623722e-03 -6.18234932e-04]\n",
      " [ 3.23473980e-02  9.83692190e+00 -2.30584732e-01 -4.02692034e-03\n",
      "  -2.84306156e-03 -5.15878153e-04]\n",
      " [-8.84506775e-03  9.76348794e+00 -2.43965062e-01 -4.05230477e-03\n",
      "  -2.37222066e-03 -2.64735568e-03]\n",
      " [-8.84506775e-03  9.76348794e+00 -2.43965062e-01 -4.04821050e-03\n",
      "  -2.36239440e-03 -2.61542037e-03]\n",
      " [ 1.40474803e-02  9.79841246e+00 -2.65295039e-01 -3.89672252e-03\n",
      "  -3.21072755e-03 -6.69003895e-04]\n",
      " [ 1.40474803e-02  9.79841246e+00 -2.65295039e-01 -4.07441388e-03\n",
      "  -3.18534307e-03 -8.24586195e-04]\n",
      " [ 4.97615245e-02  9.81465519e+00 -2.22705690e-01 -4.41341951e-03\n",
      "  -4.13193849e-03 -5.24066662e-05]\n",
      " [ 4.97615245e-02  9.81465519e+00 -2.22705690e-01 -4.48957296e-03\n",
      "  -4.02876283e-03  4.99501129e-05]\n",
      " [ 1.41533833e-03  9.77612651e+00 -2.45633942e-01 -4.70165617e-03\n",
      "  -3.90839105e-03 -2.12820211e-03]\n",
      " [ 1.41533833e-03  9.77612651e+00 -2.45633942e-01 -4.70165617e-03\n",
      "  -3.90839105e-03 -2.12820211e-03]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train.mean(axis = 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "026a712b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[-1.73691452e-01, -1.72058098e-01,  2.34306713e-01,\n",
       "         -1.39951591e+00,  2.20522082e-01, -6.69991927e-02],\n",
       "        [-1.73691452e-01, -1.72058098e-01,  2.34306713e-01,\n",
       "         -1.39951591e+00,  2.20522082e-01, -6.69991927e-02],\n",
       "        [ 6.49969579e-01,  1.17835238e+00,  7.62003869e-01,\n",
       "          4.36276370e-01,  1.16010689e-02, -1.67463308e-01],\n",
       "        ...,\n",
       "        [-3.62496261e-01,  9.76880935e-01, -1.92786303e-01,\n",
       "         -5.15615899e-01,  7.15594121e-03, -5.19087715e-01],\n",
       "        [ 6.99530853e-01, -2.86408457e-01, -4.20569257e-01,\n",
       "         -1.26353131e+00,  1.82687622e-02, -7.70248016e-01],\n",
       "        [ 6.99530853e-01, -2.86408457e-01, -4.20569257e-01,\n",
       "         -1.26353131e+00,  1.82687622e-02, -7.70248016e-01]],\n",
       "\n",
       "       [[ 1.34146719e+00,  3.18010222e-01,  1.32197029e+00,\n",
       "         -1.60349286e+00, -1.25303785e+00, -8.95828167e-01],\n",
       "        [ 1.34146719e+00,  3.18010222e-01,  1.32197029e+00,\n",
       "         -1.60349286e+00, -1.25303785e+00, -8.95828167e-01],\n",
       "        [ 2.45541558e+00,  3.83352664e-01,  1.50229836e+00,\n",
       "          1.64307149e-01, -1.39083686e+00,  1.36461439e+00],\n",
       "        ...,\n",
       "        [ 1.98340353e+00,  3.56125562e-01, -2.80103106e-01,\n",
       "         -1.16154288e+00, -2.88445077e-01,  1.69112283e+00],\n",
       "        [ 1.31314650e+00, -7.43805555e-01, -4.68024037e-01,\n",
       "         -7.19592800e-01, -5.24036890e-01, -4.93971669e-01],\n",
       "        [ 1.31314650e+00, -7.43805555e-01, -4.68024037e-01,\n",
       "         -7.19592800e-01, -5.24036890e-01, -4.93971669e-01]],\n",
       "\n",
       "       [[-1.45284407e+00, -1.38633957e+00, -8.00207451e-01,\n",
       "          5.04268683e-01,  1.97634770e+00, -5.94435775e-01],\n",
       "        [-1.45284407e+00, -1.38633957e+00, -8.00207451e-01,\n",
       "          5.04268683e-01,  1.97634770e+00, -5.94435775e-01],\n",
       "        [-7.33025704e-01,  1.58674373e+00,  7.86680330e-01,\n",
       "          6.23186921e-02,  1.93411893e+00, -9.96292273e-01],\n",
       "        ...,\n",
       "        [-1.41272309e+00, -9.45279171e-01, -4.18671068e-01,\n",
       "         -7.36659214e-02,  1.89189037e+00, -9.71176227e-01],\n",
       "        [-1.28291978e+00, -2.04729319e-01, -7.13020681e-02,\n",
       "         -5.67362117e-03,  1.95856718e+00, -4.18623609e-01],\n",
       "        [-1.28291978e+00, -2.04729319e-01, -7.13020681e-02,\n",
       "         -5.67362117e-03,  1.95856718e+00, -4.18623609e-01]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 4.37564132e-01,  1.00202080e-01, -4.88904120e-01,\n",
       "         -5.49612075e-01, -1.39533294e-01,  3.85089310e-01],\n",
       "        [ 4.37564132e-01,  1.00202080e-01, -4.88904120e-01,\n",
       "         -5.49612075e-01, -1.39533294e-01,  3.85089310e-01],\n",
       "        [ 3.54962047e-01,  3.56125562e-01, -1.45331522e-01,\n",
       "          1.32017631e+00, -9.95271341e-02,  8.34889106e-03],\n",
       "        ...,\n",
       "        [-1.24130177e-01,  9.71434647e-01,  1.01433331e-01,\n",
       "          2.83225290e-02, -9.90775276e-01,  1.03810603e+00],\n",
       "        [-3.62496261e-01, -1.37544917e+00, -9.42571839e-01,\n",
       "          1.64307149e-01, -6.72948646e-01, -6.69991927e-02],\n",
       "        [-3.62496261e-01, -1.37544917e+00, -9.42571839e-01,\n",
       "          1.64307149e-01, -6.72948646e-01, -6.69991927e-02]],\n",
       "\n",
       "       [[-1.80771626e-01, -4.68195006e-02,  8.15153217e-01,\n",
       "         -2.69136966e+00,  5.60523528e-02,  4.35321363e-01],\n",
       "        [-1.80771626e-01, -4.68195006e-02,  8.15153217e-01,\n",
       "         -2.69136966e+00,  5.60523528e-02,  4.35321363e-01],\n",
       "        [ 1.23290441e+00, -2.67140978e+00,  8.29196820e-04,\n",
       "         -2.77642822e-01, -2.39599562e-02,  1.49019462e+00],\n",
       "        ...,\n",
       "        [ 3.59682163e-01,  1.87325336e-01,  5.53202832e-01,\n",
       "         -8.55577504e-01, -3.06276477e-02, -1.17231255e-01],\n",
       "        [-1.06815428e+00, -1.12496980e+00, -6.56075002e-02,\n",
       "         -2.65737349e+00, -6.17944341e-03, -2.00093340e+00],\n",
       "        [-1.06815428e+00, -1.12496980e+00, -6.56075002e-02,\n",
       "         -2.65737349e+00, -6.17944341e-03, -2.00093340e+00]],\n",
       "\n",
       "       [[-2.13812466e-01, -4.55208682e-01, -9.02709769e-01,\n",
       "          2.66295606e-01, -6.17944341e-03,  3.34649198e-02],\n",
       "        [-2.13812466e-01, -4.55208682e-01, -9.02709769e-01,\n",
       "          2.66295606e-01, -6.17944341e-03,  3.34649198e-02],\n",
       "        [-4.62481932e-02, -3.89866240e-01, -6.38861265e-01,\n",
       "          2.32299453e-01, -8.40200748e-03,  5.85809485e-02],\n",
       "        ...,\n",
       "        [ 1.80317586e-01,  1.04766750e+00,  3.27318078e-01,\n",
       "          8.78226386e-01, -2.84050839e-02, -1.67463308e-01],\n",
       "        [-3.24735305e-01,  3.01673527e-01, -1.81527200e-02,\n",
       "          4.36276370e-01, -3.72953393e-02, -7.45131971e-01],\n",
       "        [-3.24735305e-01,  3.01673527e-01, -1.81527200e-02,\n",
       "          4.36276370e-01, -3.72953393e-02, -7.45131971e-01]]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5bbc1991",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.02073677 -0.04503055  0.03220533 -0.00767875 -0.01889513 -0.01252502]\n",
      " [-0.02073677 -0.04503055  0.03220533 -0.00786104 -0.01890705 -0.01074064]\n",
      " [ 0.00247155  0.00392522  0.00880555 -0.0120536  -0.01460791  0.01801149]\n",
      " [ 0.00247155  0.00392522  0.00880555 -0.02148684 -0.01487902  0.01201866]\n",
      " [-0.00975584  0.0842311   0.0244828   0.01961842 -0.01164945 -0.01511742]\n",
      " [-0.00975584  0.0842311   0.0244828   0.0153803  -0.01127406 -0.01090897]\n",
      " [-0.04093332 -0.04522035  0.0044446  -0.01287388 -0.00324185 -0.03147988]\n",
      " [-0.04093332 -0.04522035  0.0044446  -0.01246374 -0.00319418 -0.03195123]\n",
      " [-0.01495683  0.01736304  0.00021598 -0.01260045  0.00148036  0.01464473]\n",
      " [-0.01495683  0.01736304  0.00021598 -0.02362869  0.00165018  0.00922425]\n",
      " [-0.01089791  0.08301212  0.02776774  0.03215052  0.0069027   0.00484746]\n",
      " [-0.01089791  0.08301212  0.02776774  0.02873267  0.00730789  0.00942625]\n",
      " [-0.05221795 -0.08008863 -0.01916574  0.04222177  0.01452079 -0.04565394]\n",
      " [-0.05221795 -0.08008863 -0.01916574  0.04263191  0.01456846 -0.04612528]\n",
      " [-0.03455546  0.01882289 -0.00470284  0.06336682  0.01889442  0.01147998]\n",
      " [-0.03455546  0.01882289 -0.00470284  0.05393357  0.0186233   0.00548715]\n",
      " [-0.01398244  0.0594503  -0.01198741  0.02818582  0.02370898 -0.00925926]\n",
      " [-0.01398244  0.0594503  -0.01198741  0.02394769  0.02408437 -0.00505081]\n",
      " [-0.0420912  -0.0557385  -0.01559041 -0.01893485  0.02792172 -0.04780867]\n",
      " [-0.0420912  -0.0557385  -0.01559041 -0.018707    0.02795747 -0.04649563]\n",
      " [-0.01558639 -0.0050455  -0.02060369 -0.01866143  0.02858909  0.01518341]\n",
      " [-0.01558639 -0.0050455  -0.02060369 -0.02809468  0.02831797  0.00919058]\n",
      " [ 0.02675865  0.03522424  0.00176048 -0.04112806  0.02267814  0.06942192]\n",
      " [ 0.02675865  0.03522424  0.00176048 -0.0435889   0.02306843  0.07150931]\n",
      " [-0.03087933 -0.06549027 -0.02295958  0.00726861  0.02108123 -0.03969477]\n",
      " [-0.03087933 -0.06549027 -0.02295958  0.00749647  0.02111698 -0.03838174]\n",
      " [ 0.0185807  -0.00548347 -0.00900017  0.00416977  0.01560228  0.02211894]\n",
      " [ 0.0185807  -0.00548347 -0.00900017  0.00362291  0.01516134  0.02094057]\n",
      " [ 0.04331388  0.06061086 -0.01054214  0.00553691  0.00924146  0.01077296]\n",
      " [ 0.04331388  0.06061086 -0.01054214  0.00307606  0.00963175  0.01286035]\n",
      " [ 0.00965296 -0.03656347 -0.00921932  0.00371406  0.00339009 -0.00966327]\n",
      " [ 0.00965296 -0.03656347 -0.00921932  0.00394191  0.00342585 -0.00835024]\n",
      " [ 0.04359227  0.00652376  0.00261734  0.02093999 -0.00328058  0.04154514]\n",
      " [ 0.04359227  0.00652376  0.00261734  0.01264602 -0.00362915  0.03457595]\n",
      " [ 0.04953355  0.05045769  0.01044738  0.00863575 -0.01088377  0.00063901]\n",
      " [ 0.04953355  0.05045769  0.01044738  0.0061749  -0.01049348  0.0027264 ]\n",
      " [-0.0216384  -0.04224225  0.00034543 -0.02859596 -0.01305271 -0.06625851]\n",
      " [-0.0216384  -0.04224225  0.00034543 -0.0283681  -0.01301696 -0.06494547]\n",
      " [ 0.02210181 -0.00143972  0.00582913 -0.00726861 -0.01696752  0.03174787]\n",
      " [ 0.02210181 -0.00143972  0.00582913 -0.01670186 -0.01723864  0.02575504]\n",
      " [ 0.03283913  0.08124574  0.00973238  0.00818004 -0.01912156  0.03316191]\n",
      " [ 0.03283913  0.08124574  0.00973238  0.00394191 -0.01874617  0.03737036]\n",
      " [-0.00776593 -0.08576745 -0.00087593  0.00252921 -0.01703306 -0.0502664 ]\n",
      " [-0.00776593 -0.08576745 -0.00087593  0.00275706 -0.01699731 -0.04895336]\n",
      " [ 0.01480017 -0.0063375  -0.01778694  0.01118774 -0.02008388  0.03107452]\n",
      " [ 0.01480017 -0.0063375  -0.01778694  0.00129878 -0.01999152  0.02467768]\n",
      " [ 0.05000493  0.03060385  0.01597911 -0.01756772 -0.0234356   0.05642622]\n",
      " [ 0.05000493  0.03060385  0.01597911 -0.02180584 -0.02306021  0.06063467]\n",
      " [ 0.00234816 -0.05702314 -0.00219906 -0.0336088  -0.02262225 -0.02892114]\n",
      " [ 0.00234816 -0.05702314 -0.00219906 -0.0336088  -0.02262225 -0.02892114]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train_scaled.mean(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d823b1bc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.99003579 0.99421657 0.95896616 1.03908338 1.01138777 0.99208408]\n",
      " [0.99003579 0.99421657 0.95896616 1.0391009  1.01138775 0.99191672]\n",
      " [1.00905402 1.05106109 1.04489979 1.00446655 1.00507414 1.03405625]\n",
      " [1.00905402 1.05106109 1.04489979 1.00836567 1.00530754 1.03196417]\n",
      " [1.00036155 1.0202067  0.98993738 0.97283473 1.00366037 0.99648565]\n",
      " [1.00036155 1.0202067  0.98993738 0.97465151 1.00290797 0.9978889 ]\n",
      " [1.00558864 0.97633112 0.99053622 1.03065736 1.00381104 1.01257265]\n",
      " [1.00558864 0.97633112 0.99053622 1.03062632 1.00389596 1.01355898]\n",
      " [0.98705479 1.00009992 1.00949451 0.97482249 1.0007473  1.01836133]\n",
      " [0.98705479 1.00009992 1.00949451 0.97911111 1.00074772 1.01702981]\n",
      " [0.98684    0.99312401 0.96419992 0.96888081 0.9990446  0.99370595]\n",
      " [0.98684    0.99312401 0.96419992 0.97110436 0.99828134 0.99509846]\n",
      " [0.9961535  1.01162018 1.00205236 1.00060831 0.99878288 0.99707244]\n",
      " [0.9961535  1.01162018 1.00205236 1.00055376 0.99886739 0.99806739]\n",
      " [0.99308007 1.02416987 1.03243432 0.98467322 0.99634257 1.02150073]\n",
      " [0.99308007 1.02416987 1.03243432 0.98936978 0.99658713 1.01934448]\n",
      " [0.98078034 0.97516101 0.96907322 0.98740135 0.99708754 0.99829172]\n",
      " [0.98078034 0.97516101 0.96907322 0.98922808 0.99631686 0.99966777]\n",
      " [0.97629903 0.98723255 0.96443163 1.02156441 0.99661733 0.98441134]\n",
      " [0.97629903 0.98723255 0.96443163 1.02155142 0.99670192 0.98531431]\n",
      " [1.01636513 1.01827788 1.04453334 0.9871182  0.995226   1.01621578]\n",
      " [1.01636513 1.01827788 1.04453334 0.99102268 0.99547347 1.01407019]\n",
      " [0.99390493 0.98918329 0.99239385 0.97261217 0.99499775 0.96104644]\n",
      " [0.99390493 0.98918329 0.99239385 0.97203176 0.99422458 0.96241162]\n",
      " [0.99157506 1.00819751 1.00264928 1.02068321 0.99723841 0.97996242]\n",
      " [0.99157506 1.00819751 1.00264928 1.02066436 0.99732319 0.98085862]\n",
      " [0.99854716 1.01304047 1.03984593 0.98126032 0.99211768 1.01445477]\n",
      " [0.99854716 1.01304047 1.03984593 0.98033055 0.9923643  1.01424042]\n",
      " [0.99042531 0.97812567 0.98078726 0.99164418 0.99018523 0.98475341]\n",
      " [0.99042531 0.97812567 0.98078726 0.99119078 0.9894136  0.9862099 ]\n",
      " [1.01054483 1.01461838 1.00035541 1.04581843 0.99159344 1.03350688]\n",
      " [1.01054483 1.01461838 1.00035541 1.04580081 0.99167935 1.03431857]\n",
      " [1.0262329  1.02141016 1.02670898 0.9921252  0.99325411 1.00633521]\n",
      " [1.0262329  1.02141016 1.02670898 0.99642306 0.99349029 1.00370456]\n",
      " [1.00055798 0.96315362 0.9995652  0.96005026 0.99702973 0.97335344]\n",
      " [1.00055798 0.96315362 0.9995652  0.95958988 0.99627128 0.97484867]\n",
      " [0.99483462 0.9686152  0.9900141  1.00386031 0.99908389 0.97632638]\n",
      " [0.99483462 0.9686152  0.9900141  1.00384928 0.99916973 0.9772616 ]\n",
      " [1.01636743 1.00104035 1.01543427 0.98893651 0.99951048 1.01807872]\n",
      " [1.01636743 1.00104035 1.01543427 0.99294208 0.99974454 1.01603476]\n",
      " [1.00748606 0.95980546 0.98058766 0.96121811 1.00407978 0.98645722]\n",
      " [1.00748606 0.95980546 0.98058766 0.96300646 1.00333049 0.98766902]\n",
      " [0.9963729  0.99344396 1.0068312  1.03625051 1.00614627 0.98357838]\n",
      " [0.9963729  0.99344396 1.0068312  1.03623299 1.00623165 0.98448539]\n",
      " [1.02936598 1.01747854 1.02607488 0.98282136 1.00667765 1.02412761]\n",
      " [1.02936598 1.01747854 1.02607488 0.98739243 1.0066774  1.02227123]\n",
      " [0.99302591 0.99122412 0.97753576 0.99218834 1.0085834  0.9865042 ]\n",
      " [0.99302591 0.99122412 0.97753576 0.99381117 1.00783907 0.98761682]\n",
      " [0.99617373 0.9909612  0.98019119 1.06421685 1.00958484 0.98657954]\n",
      " [0.99617373 0.9909612  0.98019119 1.06421685 1.00958484 0.98657954]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train_scaled.std(axis = 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "22036613",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_scaled = scaler.transform(X_test.reshape(-1, X_test.shape[-1])).reshape(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "33694c87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(321, 50, 6)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ca348d68",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.00095857 -0.08820927 -0.11907691  0.00417573  0.02676436 -0.04188317]\n",
      " [-0.00095857 -0.08820927 -0.11907691  0.0051289   0.02687514 -0.04297857]\n",
      " [ 0.02620786  0.03652165 -0.07061611  0.06390729  0.01432908  0.01750333]\n",
      " [ 0.02620786  0.03652165 -0.07061611  0.06390729  0.01432908  0.01750333]\n",
      " [ 0.09437022  0.01072056 -0.05624663 -0.03564531  0.00456641  0.09488574]\n",
      " [ 0.09437022  0.01072056 -0.05624663 -0.03564531  0.00456641  0.09488574]\n",
      " [ 0.04636763 -0.08999035 -0.06091819  0.01656685 -0.00951675  0.00161999]\n",
      " [ 0.04636763 -0.08999035 -0.06091819  0.01614323 -0.00954445  0.00576687]\n",
      " [ 0.06762287  0.00230678 -0.05388276  0.06020054 -0.0229006   0.02360629]\n",
      " [ 0.06762287  0.00230678 -0.05388276  0.06390729 -0.02392534  0.02227616]\n",
      " [ 0.09732581  0.01911739 -0.06091819 -0.06932374 -0.0384724   0.04598382]\n",
      " [ 0.09732581  0.01911739 -0.06091819 -0.07123007 -0.03854164  0.04512314]\n",
      " [ 0.07234299 -0.00361348 -0.00351121 -0.11592283 -0.05072766  0.03565573]\n",
      " [ 0.07234299 -0.00361348 -0.00351121 -0.11634646 -0.05075536  0.03980261]\n",
      " [ 0.113236   -0.00627665 -0.04283585 -0.12365404 -0.06332911  0.03307371]\n",
      " [ 0.113236   -0.00627665 -0.04283585 -0.12365404 -0.06332911  0.03307371]\n",
      " [ 0.10594996  0.07356943  0.02934346 -0.05799169 -0.07749536  0.0806455 ]\n",
      " [ 0.10594996  0.07356943  0.02934346 -0.05799169 -0.07749536  0.0806455 ]\n",
      " [ 0.04952172 -0.06174654 -0.01447534  0.02588667 -0.0817397   0.03948964]\n",
      " [ 0.04952172 -0.06174654 -0.01447534  0.02588667 -0.0817397   0.03948964]\n",
      " [ 0.07006381  0.04873517 -0.00536801  0.07121487 -0.08593557  0.02391926]\n",
      " [ 0.07006381  0.04873517 -0.00536801  0.07121487 -0.08593557  0.02391926]\n",
      " [ 0.01025356  0.12775009 -0.00504278  0.10605828 -0.07494738 -0.10510358]\n",
      " [ 0.01025356  0.12775009 -0.00504278  0.10192791 -0.074982   -0.10017426]\n",
      " [ 0.02489916 -0.03777737  0.00540023 -0.03384488 -0.06580093  0.02305859]\n",
      " [ 0.02489916 -0.03777737  0.00540023 -0.03384488 -0.06580093  0.02305859]\n",
      " [-0.00811962  0.04747993 -0.03388966  0.01963816 -0.05567823  0.0065493 ]\n",
      " [-0.00811962  0.04747993 -0.03388966 -0.00101371 -0.05528357 -0.00463946]\n",
      " [-0.02836761  0.0613559   0.02146685 -0.00323776 -0.04367915  0.03025696]\n",
      " [-0.02836761  0.0613559   0.02146685 -0.00736813 -0.04371377  0.03518627]\n",
      " [-0.07105456 -0.10186469 -0.01576371 -0.02960861 -0.02463849 -0.04767316]\n",
      " [-0.07105456 -0.10186469 -0.01576371 -0.02960861 -0.02463849 -0.04767316]\n",
      " [-0.06681969  0.02091539 -0.05006125 -0.02230103 -0.0117047  -0.03797101]\n",
      " [-0.06681969  0.02091539 -0.05006125 -0.0249487  -0.01152468 -0.03570196]\n",
      " [-0.04421163  0.08685164 -0.01768556 -0.01171032  0.00314702  0.05224326]\n",
      " [-0.04421163  0.08685164 -0.01768556 -0.0158407   0.0031124   0.05717257]\n",
      " [-0.00059096 -0.08866728 -0.02563904  0.04452631  0.01361592  0.08150617]\n",
      " [-0.00059096 -0.08866728 -0.02563904  0.04452631  0.01361592  0.08150617]\n",
      " [-0.01536155  0.04020272 -0.05846562  0.04823305  0.02008974 -0.01410687]\n",
      " [-0.01536155  0.04020272 -0.05846562  0.04823305  0.02008974 -0.01410687]\n",
      " [-0.00659771  0.01161957 -0.01737215 -0.00175506  0.02231923 -0.02279185]\n",
      " [-0.00659771  0.01161957 -0.01737215 -0.00175506  0.02231923 -0.02279185]\n",
      " [-0.03434496  0.01112767 -0.02297211 -0.02854954  0.02285929  0.04402773]\n",
      " [-0.03434496  0.01112767 -0.02297211 -0.02854954  0.02285929  0.04402773]\n",
      " [ 0.00206319  0.05519826 -0.00157754  0.00343438  0.02733904 -0.01230728]\n",
      " [ 0.00206319  0.05519826 -0.00157754  0.00449345  0.02649433 -0.01136836]\n",
      " [-0.04669668  0.13294084 -0.0277442   0.05850603  0.03230346 -0.0753712 ]\n",
      " [-0.04669668  0.13294084 -0.0277442   0.05850603  0.03230346 -0.0753712 ]\n",
      " [-0.05750443 -0.06111894 -0.02112123  0.05321068  0.03573078 -0.00534365]\n",
      " [-0.05750443 -0.06111894 -0.02112123  0.05374022  0.03581386 -0.00229217]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test_scaled.mean(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "789e3bf7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.93923015e-03  9.76241430e+00 -3.93052778e-01 -4.02271888e-03\n",
      "   9.66537224e-03 -2.44346099e-03]\n",
      " [-1.93923015e-03  9.76241430e+00 -3.93052778e-01 -4.00559182e-03\n",
      "   9.69582038e-03 -2.47010309e-03]\n",
      " [ 2.56201955e-02  9.81725718e+00 -3.31928842e-01 -2.94942300e-03\n",
      "   6.24757184e-03 -9.99078686e-04]\n",
      " [ 2.56201955e-02  9.81725718e+00 -3.31928842e-01 -2.94942300e-03\n",
      "   6.24757184e-03 -9.99078686e-04]\n",
      " [ 9.47686216e-02  9.80591271e+00 -3.13804513e-01 -4.73824954e-03\n",
      "   3.56433230e-03  8.82995273e-04]\n",
      " [ 9.47686216e-02  9.80591271e+00 -3.13804513e-01 -4.73824954e-03\n",
      "   3.56433230e-03  8.82995273e-04]\n",
      " [ 4.60716019e-02  9.76163117e+00 -3.19696785e-01 -3.80006708e-03\n",
      "  -3.06384010e-04 -1.38538911e-03]\n",
      " [ 4.60716019e-02  9.76163117e+00 -3.19696785e-01 -3.80767911e-03\n",
      "  -3.13996038e-04 -1.28452974e-03]\n",
      " [ 6.76343321e-02  9.80221326e+00 -3.10822945e-01 -3.01602823e-03\n",
      "  -3.98489690e-03 -8.50644166e-04]\n",
      " [ 6.76343321e-02  9.80221326e+00 -3.10822945e-01 -2.94942299e-03\n",
      "  -4.26654194e-03 -8.82995283e-04]\n",
      " [ 9.77669654e-02  9.80960471e+00 -3.19696785e-01 -5.34340579e-03\n",
      "  -8.26475978e-03 -3.06384060e-04]\n",
      " [ 9.77669654e-02  9.80960471e+00 -3.19696785e-01 -5.37765991e-03\n",
      "  -8.28378985e-03 -3.27317137e-04]\n",
      " [ 7.24227361e-02  9.79961018e+00 -2.47288964e-01 -6.18072884e-03\n",
      "  -1.16330820e-02 -5.57581042e-04]\n",
      " [ 7.24227361e-02  9.79961018e+00 -2.47288964e-01 -6.18834087e-03\n",
      "  -1.16406940e-02 -4.56721671e-04]\n",
      " [ 1.13907315e-01  9.79843922e+00 -2.96889402e-01 -6.31964834e-03\n",
      "  -1.50965548e-02 -6.20380257e-04]\n",
      " [ 1.13907315e-01  9.79843922e+00 -2.96889402e-01 -6.31964834e-03\n",
      "  -1.50965548e-02 -6.20380257e-04]\n",
      " [ 1.06515871e-01  9.83354671e+00 -2.05849139e-01 -5.13978402e-03\n",
      "  -1.89901061e-02  5.36647979e-04]\n",
      " [ 1.06515871e-01  9.83354671e+00 -2.05849139e-01 -5.13978402e-03\n",
      "  -1.89901061e-02  5.36647979e-04]\n",
      " [ 4.92713256e-02  9.77404968e+00 -2.61118090e-01 -3.63260242e-03\n",
      "  -2.01566500e-02 -4.64333656e-04]\n",
      " [ 4.92713256e-02  9.77404968e+00 -2.61118090e-01 -3.63260242e-03\n",
      "  -2.01566500e-02 -4.64333656e-04]\n",
      " [ 7.01105792e-02  9.82262734e+00 -2.49630953e-01 -2.81811550e-03\n",
      "  -2.13098724e-02 -8.43032098e-04]\n",
      " [ 7.01105792e-02  9.82262734e+00 -2.49630953e-01 -2.81811550e-03\n",
      "  -2.13098724e-02 -8.43032098e-04]\n",
      " [ 9.43509330e-03  9.85736938e+00 -2.49220736e-01 -2.19202619e-03\n",
      "  -1.82898005e-02 -3.98109066e-03]\n",
      " [ 9.43509330e-03  9.85736938e+00 -2.49220736e-01 -2.26624347e-03\n",
      "  -1.82993156e-02 -3.86120122e-03]\n",
      " [ 2.42925678e-02  9.78458867e+00 -2.36048896e-01 -4.70589835e-03\n",
      "  -1.57759275e-02 -8.63965220e-04]\n",
      " [ 2.42925678e-02  9.78458867e+00 -2.36048896e-01 -4.70589835e-03\n",
      "  -1.57759275e-02 -8.63965220e-04]\n",
      " [-9.20387808e-03  9.82207543e+00 -2.85605511e-01 -3.74487988e-03\n",
      "  -1.29937316e-02 -1.26549971e-03]\n",
      " [-9.20387808e-03  9.82207543e+00 -2.85605511e-01 -4.11596624e-03\n",
      "  -1.28852602e-02 -1.53762971e-03]\n",
      " [-2.97447867e-02  9.82817654e+00 -2.15783956e-01 -4.15592942e-03\n",
      "  -9.69582063e-03 -6.88888465e-04]\n",
      " [-2.97447867e-02  9.82817654e+00 -2.15783956e-01 -4.23014669e-03\n",
      "  -9.70533566e-03 -5.68999026e-04]\n",
      " [-7.30492575e-02  9.75641015e+00 -2.62743127e-01 -4.62977813e-03\n",
      "  -4.46255102e-03 -2.58428353e-03]\n",
      " [-7.30492575e-02  9.75641015e+00 -2.62743127e-01 -4.62977813e-03\n",
      "  -4.46255102e-03 -2.58428353e-03]\n",
      " [-6.87531205e-02  9.81039527e+00 -3.06002843e-01 -4.49847070e-03\n",
      "  -9.07735101e-04 -2.34831060e-03]\n",
      " [-6.87531205e-02  9.81039527e+00 -3.06002843e-01 -4.54604588e-03\n",
      "  -8.58256918e-04 -2.29312340e-03]\n",
      " [-4.58180085e-02  9.83938676e+00 -2.65167163e-01 -4.30816998e-03\n",
      "   3.17421591e-03 -1.54143542e-04]\n",
      " [-4.58180085e-02  9.83938676e+00 -2.65167163e-01 -4.38238725e-03\n",
      "   3.16470087e-03 -3.42541027e-05]\n",
      " [-1.56630115e-03  9.76221291e+00 -2.75198944e-01 -3.29767323e-03\n",
      "   6.05156252e-03  5.57581072e-04]\n",
      " [-1.56630115e-03  9.76221291e+00 -2.75198944e-01 -3.29767323e-03\n",
      "   6.05156252e-03  5.57581072e-04]\n",
      " [-1.65505730e-02  9.81887571e+00 -3.16603338e-01 -3.23106804e-03\n",
      "   7.83087336e-03 -1.76789342e-03]\n",
      " [-1.65505730e-02  9.81887571e+00 -3.16603338e-01 -3.23106804e-03\n",
      "   7.83087336e-03 -1.76789342e-03]\n",
      " [-7.65995149e-03  9.80630800e+00 -2.64771858e-01 -4.12928731e-03\n",
      "   8.44364191e-03 -1.97912721e-03]\n",
      " [-7.65995149e-03  9.80630800e+00 -2.64771858e-01 -4.12928731e-03\n",
      "   8.44364191e-03 -1.97912721e-03]\n",
      " [-3.58086069e-02  9.80609172e+00 -2.71835126e-01 -4.61074806e-03\n",
      "   8.59207683e-03 -3.53959339e-04]\n",
      " [-3.58086069e-02  9.80609172e+00 -2.71835126e-01 -4.61074806e-03\n",
      "   8.59207683e-03 -3.53959339e-04]\n",
      " [ 1.12624531e-03  9.82546910e+00 -2.44850009e-01 -4.03603996e-03\n",
      "   9.82332227e-03 -1.72412431e-03]\n",
      " [ 1.12624531e-03  9.82546910e+00 -2.44850009e-01 -4.01700989e-03\n",
      "   9.59115542e-03 -1.70128823e-03]\n",
      " [-4.83390045e-02  9.85965170e+00 -2.77854195e-01 -3.04647636e-03\n",
      "   1.11877789e-02 -3.25794792e-03]\n",
      " [-4.83390045e-02  9.85965170e+00 -2.77854195e-01 -3.04647636e-03\n",
      "   1.11877789e-02 -3.25794792e-03]\n",
      " [-5.93031099e-02  9.77432563e+00 -2.69500595e-01 -3.14162666e-03\n",
      "   1.21297666e-02 -1.55475673e-03]\n",
      " [-5.93031099e-02  9.77432563e+00 -2.69500595e-01 -3.13211162e-03\n",
      "   1.21526027e-02 -1.48053947e-03]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test.mean(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "dc639ec9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.00452509 0.99031489 1.02207891 0.998118   0.97627642 0.99053266]\n",
      " [1.00452509 0.99031489 1.02207891 0.99802697 0.97647593 0.99286243]\n",
      " [1.00287348 0.9478526  0.97597252 0.91315594 0.98549693 0.95786853]\n",
      " [1.00287348 0.9478526  0.97597252 0.91315594 0.98549693 0.95786853]\n",
      " [0.98949083 0.91000021 0.97231808 0.97184616 0.99042051 0.98527446]\n",
      " [0.98949083 0.91000021 0.97231808 0.97184616 0.99042051 0.98527446]\n",
      " [0.96538464 1.03216926 0.96226772 1.0180377  0.99500426 0.94102103]\n",
      " [0.96538464 1.03216926 0.96226772 1.01808931 0.99500448 0.94054336]\n",
      " [1.05066858 1.07096928 1.06269582 0.98841193 0.99576652 0.99660133]\n",
      " [1.05066858 1.07096928 1.06269582 0.98745627 0.99629758 0.99469148]\n",
      " [1.02030043 0.98024673 1.02994029 0.98104274 1.00058071 0.99640975]\n",
      " [1.02030043 0.98024673 1.02994029 0.98005793 1.00057848 0.99627917]\n",
      " [0.98503987 0.94940493 0.93501846 1.07991641 1.00522961 0.97704484]\n",
      " [0.98503987 0.94940493 0.93501846 1.07991309 1.00522869 0.97644026]\n",
      " [1.03232054 1.02168972 1.01092869 0.96239848 1.00383435 0.98972776]\n",
      " [1.03232054 1.02168972 1.01092869 0.96239848 1.00383435 0.98972776]\n",
      " [1.03365497 1.02248453 1.02078707 0.93935001 1.00225701 0.9841319 ]\n",
      " [1.03365497 1.02248453 1.02078707 0.93935001 1.00225701 0.9841319 ]\n",
      " [1.03275773 1.00869735 1.02272142 1.04104999 1.00752286 1.00627367]\n",
      " [1.03275773 1.00869735 1.02272142 1.04104999 1.00752286 1.00627367]\n",
      " [0.98347905 1.03459201 0.98208676 0.9700004  1.00419634 1.00245346]\n",
      " [0.98347905 1.03459201 0.98208676 0.9700004  1.00419634 1.00245346]\n",
      " [1.00877279 0.98637238 0.96687275 0.97064903 1.0073224  1.06069014]\n",
      " [1.00877279 0.98637238 0.96687275 0.97623515 1.00732191 1.06091411]\n",
      " [1.00092297 0.95897292 0.93646036 1.04280234 1.00764793 1.0185194 ]\n",
      " [1.00092297 0.95897292 0.93646036 1.04280234 1.00764793 1.0185194 ]\n",
      " [1.02736605 1.04319182 0.98944299 0.98611859 1.01409275 1.00663765]\n",
      " [1.02736605 1.04319182 0.98944299 0.99801683 1.0141159  1.00200104]\n",
      " [1.01505826 1.00793721 0.99016301 0.93251329 1.02059105 1.01929075]\n",
      " [1.01505826 1.00793721 0.99016301 0.93784524 1.02059162 1.01886914]\n",
      " [0.95429174 0.94117723 0.95133534 0.98203518 1.02282586 0.8879602 ]\n",
      " [0.95429174 0.94117723 0.95133534 0.98203518 1.02282586 0.8879602 ]\n",
      " [0.95747709 1.02546923 1.0301692  0.95797427 1.01323397 1.02341447]\n",
      " [0.95747709 1.02546923 1.0301692  0.9577837  1.01324244 1.02498911]\n",
      " [0.98998115 1.04179388 0.9561941  1.00579857 1.00589085 1.04378881]\n",
      " [0.98998115 1.04179388 0.9561941  1.01070937 1.00589304 1.04327322]\n",
      " [0.99397303 1.04848377 0.98814765 1.07734791 1.00576886 1.02100693]\n",
      " [0.99397303 1.04848377 0.98814765 1.07734791 1.00576886 1.02100693]\n",
      " [0.98592231 1.07063719 1.05627514 0.97147263 0.9983779  0.99791161]\n",
      " [0.98592231 1.07063719 1.05627514 0.97147263 0.9983779  0.99791161]\n",
      " [0.97590583 1.04995945 1.00099625 1.01491736 0.98892188 1.0146519 ]\n",
      " [0.97590583 1.04995945 1.00099625 1.01491736 0.98892188 1.0146519 ]\n",
      " [0.99006177 0.99002461 0.94806458 1.00526106 0.98896107 1.00920821]\n",
      " [0.99006177 0.99002461 0.94806458 1.00526106 0.98896107 1.00920821]\n",
      " [0.95451047 1.03164171 1.03337088 0.98754497 0.98118816 0.98349531]\n",
      " [0.95451047 1.03164171 1.03337088 0.98669573 0.98178131 0.98309689]\n",
      " [1.00700638 0.9769607  1.00610832 0.94133374 0.97771629 1.0093378 ]\n",
      " [1.00700638 0.9769607  1.00610832 0.94133374 0.97771629 1.0093378 ]\n",
      " [0.98958907 0.99286947 1.01080053 0.93231174 0.98013478 1.0052841 ]\n",
      " [0.98958907 0.99286947 1.01080053 0.93223759 0.98033399 1.00720658]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test_scaled.std(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5789cd3a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[-1.73691452e-01, -1.72058098e-01,  2.34306713e-01,\n",
       "         -1.39951591e+00,  2.20522082e-01, -6.69991927e-02],\n",
       "        [-1.73691452e-01, -1.72058098e-01,  2.34306713e-01,\n",
       "         -1.39951591e+00,  2.20522082e-01, -6.69991927e-02],\n",
       "        [ 6.49969579e-01,  1.17835238e+00,  7.62003869e-01,\n",
       "          4.36276370e-01,  1.16010689e-02, -1.67463308e-01],\n",
       "        ...,\n",
       "        [-3.62496261e-01,  9.76880935e-01, -1.92786303e-01,\n",
       "         -5.15615899e-01,  7.15594121e-03, -5.19087715e-01],\n",
       "        [ 6.99530853e-01, -2.86408457e-01, -4.20569257e-01,\n",
       "         -1.26353131e+00,  1.82687622e-02, -7.70248016e-01],\n",
       "        [ 6.99530853e-01, -2.86408457e-01, -4.20569257e-01,\n",
       "         -1.26353131e+00,  1.82687622e-02, -7.70248016e-01]],\n",
       "\n",
       "       [[ 1.34146719e+00,  3.18010222e-01,  1.32197029e+00,\n",
       "         -1.60349286e+00, -1.25303785e+00, -8.95828167e-01],\n",
       "        [ 1.34146719e+00,  3.18010222e-01,  1.32197029e+00,\n",
       "         -1.60349286e+00, -1.25303785e+00, -8.95828167e-01],\n",
       "        [ 2.45541558e+00,  3.83352664e-01,  1.50229836e+00,\n",
       "          1.64307149e-01, -1.39083686e+00,  1.36461439e+00],\n",
       "        ...,\n",
       "        [ 1.98340353e+00,  3.56125562e-01, -2.80103106e-01,\n",
       "         -1.16154288e+00, -2.88445077e-01,  1.69112283e+00],\n",
       "        [ 1.31314650e+00, -7.43805555e-01, -4.68024037e-01,\n",
       "         -7.19592800e-01, -5.24036890e-01, -4.93971669e-01],\n",
       "        [ 1.31314650e+00, -7.43805555e-01, -4.68024037e-01,\n",
       "         -7.19592800e-01, -5.24036890e-01, -4.93971669e-01]],\n",
       "\n",
       "       [[-1.45284407e+00, -1.38633957e+00, -8.00207451e-01,\n",
       "          5.04268683e-01,  1.97634770e+00, -5.94435775e-01],\n",
       "        [-1.45284407e+00, -1.38633957e+00, -8.00207451e-01,\n",
       "          5.04268683e-01,  1.97634770e+00, -5.94435775e-01],\n",
       "        [-7.33025704e-01,  1.58674373e+00,  7.86680330e-01,\n",
       "          6.23186921e-02,  1.93411893e+00, -9.96292273e-01],\n",
       "        ...,\n",
       "        [-1.41272309e+00, -9.45279171e-01, -4.18671068e-01,\n",
       "         -7.36659214e-02,  1.89189037e+00, -9.71176227e-01],\n",
       "        [-1.28291978e+00, -2.04729319e-01, -7.13020681e-02,\n",
       "         -5.67362117e-03,  1.95856718e+00, -4.18623609e-01],\n",
       "        [-1.28291978e+00, -2.04729319e-01, -7.13020681e-02,\n",
       "         -5.67362117e-03,  1.95856718e+00, -4.18623609e-01]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 4.37564132e-01,  1.00202080e-01, -4.88904120e-01,\n",
       "         -5.49612075e-01, -1.39533294e-01,  3.85089310e-01],\n",
       "        [ 4.37564132e-01,  1.00202080e-01, -4.88904120e-01,\n",
       "         -5.49612075e-01, -1.39533294e-01,  3.85089310e-01],\n",
       "        [ 3.54962047e-01,  3.56125562e-01, -1.45331522e-01,\n",
       "          1.32017631e+00, -9.95271341e-02,  8.34889106e-03],\n",
       "        ...,\n",
       "        [-1.24130177e-01,  9.71434647e-01,  1.01433331e-01,\n",
       "          2.83225290e-02, -9.90775276e-01,  1.03810603e+00],\n",
       "        [-3.62496261e-01, -1.37544917e+00, -9.42571839e-01,\n",
       "          1.64307149e-01, -6.72948646e-01, -6.69991927e-02],\n",
       "        [-3.62496261e-01, -1.37544917e+00, -9.42571839e-01,\n",
       "          1.64307149e-01, -6.72948646e-01, -6.69991927e-02]],\n",
       "\n",
       "       [[-1.80771626e-01, -4.68195006e-02,  8.15153217e-01,\n",
       "         -2.69136966e+00,  5.60523528e-02,  4.35321363e-01],\n",
       "        [-1.80771626e-01, -4.68195006e-02,  8.15153217e-01,\n",
       "         -2.69136966e+00,  5.60523528e-02,  4.35321363e-01],\n",
       "        [ 1.23290441e+00, -2.67140978e+00,  8.29196820e-04,\n",
       "         -2.77642822e-01, -2.39599562e-02,  1.49019462e+00],\n",
       "        ...,\n",
       "        [ 3.59682163e-01,  1.87325336e-01,  5.53202832e-01,\n",
       "         -8.55577504e-01, -3.06276477e-02, -1.17231255e-01],\n",
       "        [-1.06815428e+00, -1.12496980e+00, -6.56075002e-02,\n",
       "         -2.65737349e+00, -6.17944341e-03, -2.00093340e+00],\n",
       "        [-1.06815428e+00, -1.12496980e+00, -6.56075002e-02,\n",
       "         -2.65737349e+00, -6.17944341e-03, -2.00093340e+00]],\n",
       "\n",
       "       [[-2.13812466e-01, -4.55208682e-01, -9.02709769e-01,\n",
       "          2.66295606e-01, -6.17944341e-03,  3.34649198e-02],\n",
       "        [-2.13812466e-01, -4.55208682e-01, -9.02709769e-01,\n",
       "          2.66295606e-01, -6.17944341e-03,  3.34649198e-02],\n",
       "        [-4.62481932e-02, -3.89866240e-01, -6.38861265e-01,\n",
       "          2.32299453e-01, -8.40200748e-03,  5.85809485e-02],\n",
       "        ...,\n",
       "        [ 1.80317586e-01,  1.04766750e+00,  3.27318078e-01,\n",
       "          8.78226386e-01, -2.84050839e-02, -1.67463308e-01],\n",
       "        [-3.24735305e-01,  3.01673527e-01, -1.81527200e-02,\n",
       "          4.36276370e-01, -3.72953393e-02, -7.45131971e-01],\n",
       "        [-3.24735305e-01,  3.01673527e-01, -1.81527200e-02,\n",
       "          4.36276370e-01, -3.72953393e-02, -7.45131971e-01]]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a4f30b2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled.shape[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66ab9813",
   "metadata": {},
   "source": [
    "### 3. Building LSTM Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8df235bb",
   "metadata": {},
   "source": [
    "####  3.1 Setting up the layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "50c75b98",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "Cannot convert a symbolic Tensor (LSTM_1st_layer/strided_slice:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\base.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    520\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    521\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 522\u001b[1;33m       \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    523\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    524\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprevious_value\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py\u001b[0m in \u001b[0;36madd\u001b[1;34m(self, layer)\u001b[0m\n\u001b[0;32m    211\u001b[0m           \u001b[1;31m# and create the node connecting the current layer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    212\u001b[0m           \u001b[1;31m# to the input layer we just created.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 213\u001b[1;33m           \u001b[0mlayer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    214\u001b[0m           \u001b[0mset_inputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\layers\\recurrent.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs, initial_state, constants, **kwargs)\u001b[0m\n\u001b[0;32m    666\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    667\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0minitial_state\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mconstants\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 668\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRNN\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    669\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    670\u001b[0m     \u001b[1;31m# If any of `initial_state` or `constants` are specified and are Keras\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    967\u001b[0m     \u001b[1;31m# >> model = tf.keras.Model(inputs, outputs)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    968\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0m_in_functional_construction_mode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 969\u001b[1;33m       return self._functional_construction_call(inputs, args, kwargs,\n\u001b[0m\u001b[0;32m    970\u001b[0m                                                 input_list)\n\u001b[0;32m    971\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\base_layer.py\u001b[0m in \u001b[0;36m_functional_construction_call\u001b[1;34m(self, inputs, args, kwargs, input_list)\u001b[0m\n\u001b[0;32m   1105\u001b[0m         layer=self, inputs=inputs, build_graph=True, training=training_value):\n\u001b[0;32m   1106\u001b[0m       \u001b[1;31m# Check input assumptions set after layer building, e.g. input shape.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1107\u001b[1;33m       outputs = self._keras_tensor_symbolic_call(\n\u001b[0m\u001b[0;32m   1108\u001b[0m           inputs, input_masks, args, kwargs)\n\u001b[0;32m   1109\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\base_layer.py\u001b[0m in \u001b[0;36m_keras_tensor_symbolic_call\u001b[1;34m(self, inputs, input_masks, args, kwargs)\u001b[0m\n\u001b[0;32m    838\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mnest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeras_tensor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mKerasTensor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_signature\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    839\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 840\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_infer_output_signature\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_masks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    841\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    842\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_infer_output_signature\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_masks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\base_layer.py\u001b[0m in \u001b[0;36m_infer_output_signature\u001b[1;34m(self, inputs, args, kwargs, input_masks)\u001b[0m\n\u001b[0;32m    878\u001b[0m           \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_build\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    879\u001b[0m           \u001b[0minputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_inputs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 880\u001b[1;33m           \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    881\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    882\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle_activity_regularization\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\layers\\recurrent_v2.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, inputs, mask, training, initial_state)\u001b[0m\n\u001b[0;32m   1151\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1152\u001b[0m     \u001b[1;31m# LSTM does not support constants. Ignore it during process.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1153\u001b[1;33m     \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minitial_state\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_process_inputs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minitial_state\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1154\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1155\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\layers\\recurrent.py\u001b[0m in \u001b[0;36m_process_inputs\u001b[1;34m(self, inputs, initial_state, constants)\u001b[0m\n\u001b[0;32m    866\u001b[0m         \u001b[0minitial_state\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    867\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0minitial_state\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 868\u001b[1;33m       \u001b[0minitial_state\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_initial_state\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    869\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    870\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minitial_state\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\layers\\recurrent.py\u001b[0m in \u001b[0;36mget_initial_state\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m    648\u001b[0m     \u001b[0mdtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    649\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mget_initial_state_fn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 650\u001b[1;33m       init_state = get_initial_state_fn(\n\u001b[0m\u001b[0;32m    651\u001b[0m           inputs=None, batch_size=batch_size, dtype=dtype)\n\u001b[0;32m    652\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\layers\\recurrent.py\u001b[0m in \u001b[0;36mget_initial_state\u001b[1;34m(self, inputs, batch_size, dtype)\u001b[0m\n\u001b[0;32m   2514\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2515\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mget_initial_state\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2516\u001b[1;33m     return list(_generate_zero_filled_state_for_cell(\n\u001b[0m\u001b[0;32m   2517\u001b[0m         self, inputs, batch_size, dtype))\n\u001b[0;32m   2518\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\layers\\recurrent.py\u001b[0m in \u001b[0;36m_generate_zero_filled_state_for_cell\u001b[1;34m(cell, inputs, batch_size, dtype)\u001b[0m\n\u001b[0;32m   2996\u001b[0m     \u001b[0mbatch_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2997\u001b[0m     \u001b[0mdtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2998\u001b[1;33m   \u001b[1;32mreturn\u001b[0m \u001b[0m_generate_zero_filled_state\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstate_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2999\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3000\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\layers\\recurrent.py\u001b[0m in \u001b[0;36m_generate_zero_filled_state\u001b[1;34m(batch_size_tensor, state_size, dtype)\u001b[0m\n\u001b[0;32m   3012\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3013\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mnest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_nested\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate_size\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3014\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mnest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcreate_zeros\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstate_size\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3015\u001b[0m   \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3016\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mcreate_zeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate_size\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\util\\nest.py\u001b[0m in \u001b[0;36mmap_structure\u001b[1;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[0;32m    865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    866\u001b[0m   return pack_sequence_as(\n\u001b[1;32m--> 867\u001b[1;33m       \u001b[0mstructure\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    868\u001b[0m       expand_composites=expand_composites)\n\u001b[0;32m    869\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\util\\nest.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    866\u001b[0m   return pack_sequence_as(\n\u001b[1;32m--> 867\u001b[1;33m       \u001b[0mstructure\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    868\u001b[0m       expand_composites=expand_composites)\n\u001b[0;32m    869\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\layers\\recurrent.py\u001b[0m in \u001b[0;36mcreate_zeros\u001b[1;34m(unnested_state_size)\u001b[0m\n\u001b[0;32m   3009\u001b[0m     \u001b[0mflat_dims\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtensor_shape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTensorShape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0munnested_state_size\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3010\u001b[0m     \u001b[0minit_state_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mbatch_size_tensor\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mflat_dims\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3011\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minit_state_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3012\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3013\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mnest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_nested\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate_size\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    204\u001b[0m     \u001b[1;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    205\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 206\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    207\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    208\u001b[0m       \u001b[1;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\ops\\array_ops.py\u001b[0m in \u001b[0;36mwrapped\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   2909\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2910\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2911\u001b[1;33m     \u001b[0mtensor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2912\u001b[0m     \u001b[0mtensor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_zeros_tensor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2913\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mtensor\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\ops\\array_ops.py\u001b[0m in \u001b[0;36mzeros\u001b[1;34m(shape, dtype, name)\u001b[0m\n\u001b[0;32m   2958\u001b[0m           \u001b[1;31m# Create a constant if it won't be very big. Otherwise create a fill\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2959\u001b[0m           \u001b[1;31m# op to prevent serialized GraphDefs from becoming too large.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2960\u001b[1;33m           \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_constant_if_small\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzero\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2961\u001b[0m           \u001b[1;32mif\u001b[0m \u001b[0moutput\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2962\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\ops\\array_ops.py\u001b[0m in \u001b[0;36m_constant_if_small\u001b[1;34m(value, shape, dtype, name)\u001b[0m\n\u001b[0;32m   2894\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_constant_if_small\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2895\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2896\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2897\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mconstant\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2898\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mprod\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\numpy\\core\\fromnumeric.py\u001b[0m in \u001b[0;36mprod\u001b[1;34m(a, axis, dtype, out, keepdims, initial, where)\u001b[0m\n\u001b[0;32m   3028\u001b[0m     \u001b[1;36m10\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3029\u001b[0m     \"\"\"\n\u001b[1;32m-> 3030\u001b[1;33m     return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,\n\u001b[0m\u001b[0;32m   3031\u001b[0m                           keepdims=keepdims, initial=initial, where=where)\n\u001b[0;32m   3032\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\numpy\\core\\fromnumeric.py\u001b[0m in \u001b[0;36m_wrapreduction\u001b[1;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[0;32m     85\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mreduction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mpasskwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 87\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mufunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreduce\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mpasskwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     88\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     89\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36m__array__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    866\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 867\u001b[1;33m     raise NotImplementedError(\n\u001b[0m\u001b[0;32m    868\u001b[0m         \u001b[1;34m\"Cannot convert a symbolic Tensor ({}) to a numpy array.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    869\u001b[0m         \u001b[1;34m\" This error may indicate that you're trying to pass a Tensor to\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNotImplementedError\u001b[0m: Cannot convert a symbolic Tensor (LSTM_1st_layer/strided_slice:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Building the Model\n",
    "model = Sequential()\n",
    "\n",
    "model.add(LSTM(32, return_sequences = True, input_shape = (X_train_scaled.shape[1],X_train_scaled.shape[2]),\n",
    "              kernel_regularizer = l2(0.00001), bias_regularizer = l2(0.00001), name = 'LSTM_1st_layer'))\n",
    "model.add(Dropout(0.5, name = 'Dropout_1'))\n",
    "model.add(Flatten(name = 'Flatten_2nd_layer'))\n",
    "model.add(Dropout(0.5, name = 'Dropout_2'))\n",
    "model.add(Dense(32, activation = 'relu', kernel_regularizer=l2(0.00001), bias_regularizer = l2(0.00001), name = 'Dense_3rd_layer'))\n",
    "model.add(Dropout(0.1, name = 'Dropout_3'))\n",
    "model.add(Dense(4, activation='softmax',\n",
    "               kernel_regularizer=l2(0.00001), bias_regularizer = l2(0.00001), name = 'Dense_Output_Layer'))\n",
    "model.summary()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d4a80c2",
   "metadata": {},
   "source": [
    "####  3.2 Compiling the LSTM layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4eff7a48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compiling the model\n",
    "model.compile( loss = 'categorical_crossentropy', optimizer = Adam(), metrics = ['accuracy'] )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8b72a28",
   "metadata": {},
   "source": [
    "####  3.3 Calling callbacks to use ModelCheckpoint() to save the best performing iteration/epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "87c65f74",
   "metadata": {},
   "outputs": [],
   "source": [
    "# preparing callbacks\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "\n",
    "callbacks = [ModelCheckpoint('model.h5', save_weights_only=False, save_best_only=True, verbose =1 )]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8254f0be",
   "metadata": {},
   "source": [
    "####  3.4 Fitting Model using model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "dea33358",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:855 train_function  *\n        return step_function(self, iterator)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:845 step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:1285 run\n        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:2833 call_for_each_replica\n        return self._call_for_each_replica(fn, args, kwargs)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:3608 _call_for_each_replica\n        return fn(*args, **kwargs)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:838 run_step  **\n        outputs = model.train_step(data)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:796 train_step\n        loss = self.compiled_loss(\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\compile_utils.py:204 __call__\n        loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\losses.py:155 __call__\n        losses = call_fn(y_true, y_pred)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\losses.py:259 call  **\n        return ag_fn(y_true, y_pred, **self._fn_kwargs)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py:206 wrapper\n        return target(*args, **kwargs)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\losses.py:1643 categorical_crossentropy\n        return backend.categorical_crossentropy(\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py:206 wrapper\n        return target(*args, **kwargs)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\backend.py:4862 categorical_crossentropy\n        target.shape.assert_is_compatible_with(output.shape)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\framework\\tensor_shape.py:1161 assert_is_compatible_with\n        raise ValueError(\"Shapes %s and %s are incompatible\" % (self, other))\n\n    ValueError: Shapes (None, 4) and (None, 50, 6) are incompatible\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1181\u001b[0m                 _r=1):\n\u001b[0;32m   1182\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1183\u001b[1;33m               \u001b[0mtmp_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1184\u001b[0m               \u001b[1;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1185\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    887\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    888\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 889\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    890\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    891\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    931\u001b[0m       \u001b[1;31m# This is the first call of __call__, so we have to initialize.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    932\u001b[0m       \u001b[0minitializers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 933\u001b[1;33m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_initialize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0madd_initializers_to\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minitializers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    934\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    935\u001b[0m       \u001b[1;31m# At this point we know that the initialization is complete (or less\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_initialize\u001b[1;34m(self, args, kwds, add_initializers_to)\u001b[0m\n\u001b[0;32m    761\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_graph_deleter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFunctionDeleter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lifted_initializer_graph\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    762\u001b[0m     self._concrete_stateful_fn = (\n\u001b[1;32m--> 763\u001b[1;33m         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\n\u001b[0m\u001b[0;32m    764\u001b[0m             *args, **kwds))\n\u001b[0;32m    765\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_get_concrete_function_internal_garbage_collected\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3048\u001b[0m       \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3049\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3050\u001b[1;33m       \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3051\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3052\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[1;34m(self, args, kwargs)\u001b[0m\n\u001b[0;32m   3442\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3443\u001b[0m           \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3444\u001b[1;33m           \u001b[0mgraph_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_create_graph_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3445\u001b[0m           \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprimary\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcache_key\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3446\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[1;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[0;32m   3277\u001b[0m     \u001b[0marg_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbase_arg_names\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mmissing_arg_names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3278\u001b[0m     graph_function = ConcreteFunction(\n\u001b[1;32m-> 3279\u001b[1;33m         func_graph_module.func_graph_from_py_func(\n\u001b[0m\u001b[0;32m   3280\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3281\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_python_function\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[1;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\u001b[0m\n\u001b[0;32m    997\u001b[0m         \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moriginal_func\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_decorator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munwrap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpython_func\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    998\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 999\u001b[1;33m       \u001b[0mfunc_outputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1000\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1001\u001b[0m       \u001b[1;31m# invariant: `func_outputs` contains only Tensors, CompositeTensors,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36mwrapped_fn\u001b[1;34m(*args, **kwds)\u001b[0m\n\u001b[0;32m    670\u001b[0m         \u001b[1;31m# the function a weak reference to itself to avoid a reference cycle.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    671\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcompile_with_xla\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 672\u001b[1;33m           \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__wrapped__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    673\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    674\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    984\u001b[0m           \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint:disable=broad-except\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    985\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"ag_error_metadata\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 986\u001b[1;33m               \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    987\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    988\u001b[0m               \u001b[1;32mraise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: in user code:\n\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:855 train_function  *\n        return step_function(self, iterator)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:845 step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:1285 run\n        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:2833 call_for_each_replica\n        return self._call_for_each_replica(fn, args, kwargs)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:3608 _call_for_each_replica\n        return fn(*args, **kwargs)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:838 run_step  **\n        outputs = model.train_step(data)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:796 train_step\n        loss = self.compiled_loss(\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\compile_utils.py:204 __call__\n        loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\losses.py:155 __call__\n        losses = call_fn(y_true, y_pred)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\losses.py:259 call  **\n        return ag_fn(y_true, y_pred, **self._fn_kwargs)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py:206 wrapper\n        return target(*args, **kwargs)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\losses.py:1643 categorical_crossentropy\n        return backend.categorical_crossentropy(\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py:206 wrapper\n        return target(*args, **kwargs)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\keras\\backend.py:4862 categorical_crossentropy\n        target.shape.assert_is_compatible_with(output.shape)\n    C:\\Anaconda\\envs\\tensorflow25py38\\lib\\site-packages\\tensorflow\\python\\framework\\tensor_shape.py:1161 assert_is_compatible_with\n        raise ValueError(\"Shapes %s and %s are incompatible\" % (self, other))\n\n    ValueError: Shapes (None, 4) and (None, 50, 6) are incompatible\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Fitting the model\n",
    "history = model.fit(X_train_scaled, y_train, epochs= 20, batch_size = 128, validation_data=(X_test_scaled,y_test), shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "58ac2424",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'history' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-61-6cd13d6a221b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mhistory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'history' is not defined"
     ]
    }
   ],
   "source": [
    "history.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b85e073",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_learningCurve(history, epochs):\n",
    "  # Plot training & validation accuracy values\n",
    "  epoch_range = range(1, epochs+1)\n",
    "  plt.plot(epoch_range, history.history['accuracy'])\n",
    "  plt.plot(epoch_range, history.history['val_accuracy'])\n",
    "  plt.title('Model accuracy')\n",
    "  plt.ylabel('Accuracy')\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.legend(['Train', 'Val'], loc='upper left')\n",
    "  plt.show()\n",
    "\n",
    "  # Plot training & validation loss values\n",
    "  plt.plot(epoch_range, history.history['loss'])\n",
    "  plt.plot(epoch_range, history.history['val_loss'])\n",
    "  plt.title('Model loss')\n",
    "  plt.ylabel('Loss')\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.legend(['Train', 'Val'], loc='upper left')\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4eea5f0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    " plot_learningCurve(history,20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e0444a6",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1caaed93",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "\n",
    "\n",
    "#y_pred = model.predict_classes(X_test)\n",
    "#mat = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "rounded_labels=np.argmax(y_test, axis=1)\n",
    "rounded_labels\n",
    "\n",
    "y_pred = model.predict_classes(X_test_scaled)\n",
    "mat = confusion_matrix(rounded_labels, y_pred)\n",
    "\n",
    "plot_confusion_matrix(conf_mat=mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3924b596",
   "metadata": {},
   "outputs": [],
   "source": [
    "enc.categories_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7569b881",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "y_pred = model.predict(X_test_scaled, batch_size=64, verbose=1)\n",
    "y_pred_bool = np.argmax(y_pred, axis=1)\n",
    "\n",
    "print(classification_report(rounded_labels, y_pred_bool))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01022611",
   "metadata": {},
   "source": [
    "![](f1score.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d48896d7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "enc.categories_[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19cf8816",
   "metadata": {},
   "source": [
    "## Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "333aab84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write out the Keras save file\n",
    "keras_file = \"driver_profiler_deploy_test2.h5\"\n",
    "model.save(keras_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4089ddde",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "TF_LITE_NAME = \"driver_profiler_deploy_test.tflite\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcf58deb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the model.\n",
    "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "tflite_model = converter.convert()\n"
   ]
  }
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